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Journal articles on the topic 'Agent-based computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Kwang Mong Sim. "Agent-Based Cloud Computing." IEEE Transactions on Services Computing 5, no. 4 (2012): 564–77. http://dx.doi.org/10.1109/tsc.2011.52.

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Shi, Zhongzhi, He Huang, Jiewen Luo, Fen Lin, and Haijun Zhang. "Agent-based grid computing." Applied Mathematical Modelling 30, no. 7 (2006): 629–40. http://dx.doi.org/10.1016/j.apm.2005.06.018.

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3

Aleksander, Byrski. "Agent-based computing parameters tuning." Computer Science 14, no. 3 (2013): 491. http://dx.doi.org/10.7494/csci.2013.14.3.491.

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4

Centarowicz, Krzysztof, Maciej Paszyński, David Pardo, Tibor Bosse, and Han La Poutré. "Agent-based computing, adaptive algorithms and bio computing." Procedia Computer Science 1, no. 1 (2010): 1951–52. http://dx.doi.org/10.1016/j.procs.2010.04.218.

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5

Akram, Waseem, Muaz A. Niazi, Laszlo Barna Iantovics, and Athanasios V. Vassilakos. "TOWARDS AGENT-BASED MODEL SPECIFICATION OF SMART GRID: A COGNITIVE AGENT-BASED COMPUTING APPROACH." Interdisciplinary Description of Complex Systems 17, no. 3-B (2019): 546–85. https://doi.org/10.7906/indecs.17.3.14.

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A smart grid can be considered as a complex network where each node represents a generation unit or a consumer, whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling paradigm. The agent-based modeling is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the agent-based modeling paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of t

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6

Krzywicki, D., J. Stypka, P. Anielski, et al. "Generation-free Agent-based Evolutionary Computing." Procedia Computer Science 29 (2014): 1068–77. http://dx.doi.org/10.1016/j.procs.2014.05.096.

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7

Lee, Wonjun, Anna Squicciarini, and Elisa Bertino. "Agent-based accountable grid computing systems." Journal of Supercomputing 65, no. 2 (2013): 903–29. http://dx.doi.org/10.1007/s11227-013-0871-5.

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8

Krzywicki, D., W. Turek, A. Byrski, and M. Kisiel-Dorohinicki. "Massively concurrent agent-based evolutionary computing." Journal of Computational Science 11 (November 2015): 153–62. http://dx.doi.org/10.1016/j.jocs.2015.07.003.

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9

Sim, Kwang M. "Guest Editorial: Agent-based Grid computing." Applied Intelligence 25, no. 2 (2006): 127–29. http://dx.doi.org/10.1007/s10489-006-9649-2.

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10

Hu, Jun, and Chun Guan. "An Emotional Agent Model Based on Granular Computing." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/601295.

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Affective computing has a very important significance for fulfilling intelligent information processing and harmonious communication between human being and computers. A new model for emotional agent is proposed in this paper to make agent have the ability of handling emotions, based on the granular computing theory and the traditional BDI agent model. Firstly, a new emotion knowledge base based on granular computing for emotion expression is presented in the model. Secondly, a new emotional reasoning algorithm based on granular computing is proposed. Thirdly, a new emotional agent model based

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11

Niazi, Muaz, and Amir Hussain. "Agent-based computing from multi-agent systems to agent-based models: a visual survey." Scientometrics 89, no. 2 (2011): 479–99. http://dx.doi.org/10.1007/s11192-011-0468-9.

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12

Huang, Jun, Liang Guo, Le Wang, and Zhao Qui. "The Study of Agent-Based Distributed Computing." Key Engineering Materials 460-461 (January 2011): 213–18. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.213.

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. On the basis of a case study of agent-based distributed computation, we develop a more integrative perspective on agent-based system theoretically and experientially.We construct a multi-agent system which exchange KQML message by a mobile agent based on IBM Aglet mobile developing platform and IBM JKQML.Compared with existing distributed computing model, our multi-agent example improves the computational efficiency.

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13

Dębski, Roman, Aleksander Byrski, and Marek Kisiel-Dorohinicki. "Towards an Agent-Based Augmented Cloud." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2012): 16–22. http://dx.doi.org/10.26636/jtit.2012.1.1248.

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In the paper an agent-based framework deployed in hybrid cluster and volunteer computing environment is presented. It utilizes two concepts proposed by the authors: Augmented Cloud and Agent Platform as a Service (AgPaaS). Both concepts are discussed in the context of Cloud Computing as defined by NIST. The key idea of the presented solution is to span the cloud (i.e., computing infrastructure) beyond the data center borders by utilizing web browsers as computational workers. The feasibility of the approach was demonstrated by two prototypes: the first one was based on Java Applets and Adobe F

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14

Mizera, Michal, Pawel Nowotarski, Aleksander Byrski, and Marek Kisiel-Dorohinicki. "Fine Tuning of Agent-Based Evolutionary Computing." Journal of Artificial Intelligence and Soft Computing Research 9, no. 2 (2019): 81–97. http://dx.doi.org/10.2478/jaiscr-2018-0026.

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Abstract Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

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15

Kisiel-Dorohinicki, Marek. "Flexible and efficient agent-based metaheuristic computing." Journal of Intelligent & Fuzzy Systems 37, no. 6 (2019): 7567–78. http://dx.doi.org/10.3233/jifs-179363.

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16

K., Venkateshwaran, Anu Malviya, Utkarsha Dikshit, and S. Venkatesan. "Security Framework for Agent-Based Cloud Computing." International Journal of Interactive Multimedia and Artificial Intelligence 3, no. 3 (2015): 35. http://dx.doi.org/10.9781/ijimai.2015.334.

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17

Luck, Michael. "Guest Editorial: Challenges for Agent-Based Computing." Autonomous Agents and Multi-Agent Systems 9, no. 3 (2004): 199–201. http://dx.doi.org/10.1023/b:agnt.0000038057.78313.87.

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18

Luck, Michael, and Peter McBurney. "Agent-based computing for next generation apps." ITNOW 47, no. 5 (2005): 24–25. http://dx.doi.org/10.1093/itnow/bwi101.

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19

Stypka, Jan, Piotr Anielski, Szymon Mentel, et al. "PARALLEL PATTERNS FOR AGENT-BASED EVOLUTIONARY COMPUTING." Computer Science 17, no. 1 (2016): 83. http://dx.doi.org/10.7494/csci.2016.17.1.83.

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20

De la Prieta, Fernando, Sara Rodríguez-González, Pablo Chamoso, Juan Manuel Corchado, and Javier Bajo. "Survey of agent-based cloud computing applications." Future Generation Computer Systems 100 (November 2019): 223–36. http://dx.doi.org/10.1016/j.future.2019.04.037.

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21

Ou, Chao Chun, Jia Qing Zhang, and Xiao Hua Wang. "Grid Electricity Market Bidding Model Based on Agent." Advanced Materials Research 986-987 (July 2014): 322–25. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.322.

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Grid technology can provide electricity market bidding to achieve the ability to provide large-scale computing. Agent technology has the autonomy, interoperability, and self-learning, can effectively solve the grid resource discovery and management, computing task allocation and scheduling problems, shared grid environment of heterogeneous computing resources. Introduction grid technology related content, Agent technology and proposes a use of Agent technology in the electricity market bidding model grid environment. Bidding able to provide the technology platform the parties a fair trade.

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22

Liu, Charles Z., and Manolya Kavakli. "An Agent-Aware Computing Based Mixed Reality Information System." International Journal of Virtual Reality 17, no. 3 (2017): 1–14. http://dx.doi.org/10.20870/ijvr.2017.17.3.2896.

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In this paper, we present an agent- aware computing based collaborative information sys- tem scheme for MR applications. The HCI issues in VR, AR, and MR, have been discussed firstly. Besides the equipment, the lack of understanding of users also ac- counts for a significant bottleneck of improving user ex- perience and immersion during the interaction. Aiming at the issues, an agent-aware computing based scheme is proposed and implemented. The corresponding sys- tematic scheme is presented in the views of functional- ity, modules and system workflow and discussed in de- sign, implementation,

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23

Xu, Rongxu, Wenquan Jin, and Dohyeun Kim. "Microservice Security Agent Based On API Gateway in Edge Computing." Sensors 19, no. 22 (2019): 4905. http://dx.doi.org/10.3390/s19224905.

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Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and priv

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24

Sebastian, Shoney, Iyyappan MIn, Sultan Ahmad, Mohammad Maqbool Waris, Hikmat A. M. Abdeljaber, and Jabeen Nazeer. "Hybrid Agent-Based Load-Balancing Approach Used in an IaaS Platform." Wireless Communications and Mobile Computing 2024 (April 26, 2024): 1–9. http://dx.doi.org/10.1155/2024/2357142.

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Cloud computing has received a resounding welcome. It was created following methodical and thorough study in web services, distributed computing, utility computing, and virtualization, and it offers several benefits, including lower costs, less space required, and easier management. These advantages bring in a significant number of new users to the cloud platform every day. In addition, because cloud computing is an Internet-based computing paradigm, it must deal with the issue of overwhelming demands through effective load-balancing. A very small number of studies only focus on load-balancing

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25

Ye, Jian Wei, Xu Lu Jiao, and Zhi Kai Xu. "A Two-Layer Hybrid Trust Mechanism Based on Agent Trust." Applied Mechanics and Materials 701-702 (December 2014): 923–26. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.923.

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Trust is the foundation of all kinds of Internet activity. As a more open resource sharing model, cloud computing environment consists of mass resource owners and users belonged to different control domain. Therefore, the establishment of the trust relationship among the participants and the guarantee of security and efficiency in the computing process are one of the most fundamental problems of cloud computing security. This paper proposes a two-Layer hybrid trust mechanism based on agent trust. In my proposition, trust relationship is relayed and secure computing environment is established a

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26

HOU, Jinkui, and Lei WANG. "Formal semantics of Agent-based distributed computing model." Journal of Computer Applications 33, no. 12 (2013): 3423–27. http://dx.doi.org/10.3724/sp.j.1087.2013.03423.

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27

Liutkevičius, Agnius, Nerijus Morkevičius, Algimantas Venčkauskas, and Jevgenijus Toldinas. "Distributed Agent-Based Orchestrator Model for Fog Computing." Sensors 22, no. 15 (2022): 5894. http://dx.doi.org/10.3390/s22155894.

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Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placeme

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28

Sandeep Kumar, S., S. Tarun Kumar, G. Sathraja, and J. Nagatejasri. "Multi-agent based load balancing in cloud computing." International Journal of Engineering & Technology 7, no. 1.1 (2017): 689. http://dx.doi.org/10.14419/ijet.v7i1.1.10831.

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Cloud knowledge centers area unit usually comprised of multiple servers with doubtless completely different specifications and unsteady resource usages. The challenge for these data centers is how to handle and service the millions of Requests such that the Quality of the ser-vice (Qos) is not compromised. Load balancing is an important aspect in cloud computing that involves an even work distribution among the available machines such that no machine is overloaded. This work discusses on numerous Agent primarily based load equalization tech-niques capableof equalization workloads across multip

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29

Sim, Kwang Mong. "Agent-Based Fog Computing: Gossiping, Reasoning, and Bargaining." IEEE Letters of the Computer Society 1, no. 2 (2018): 21–24. http://dx.doi.org/10.1109/locs.2018.2886828.

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30

Ratsimor, Olga, Dipanjan Chakraborty, Anupam Joshi, Timothy Finin, and Yelena Yesha. "Service Discovery in Agent-Based Pervasive Computing Environments." Mobile Networks and Applications 9, no. 6 (2004): 679–92. http://dx.doi.org/10.1023/b:mone.0000042506.96219.cc.

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31

Kowol, Michał, Aleksander Byrski, and Marek Kisiel-Dorohinicki. "Agent-based Evolutionary Computing for Difficult Discrete Problems." Procedia Computer Science 29 (2014): 1039–47. http://dx.doi.org/10.1016/j.procs.2014.05.093.

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32

Korczynski, Wojciech, Aleksander Byrski, and Marek Kisiel-Dorohinicki. "Efficient Memetic Continuous Optimization in Agent-based Computing." Procedia Computer Science 80 (2016): 845–54. http://dx.doi.org/10.1016/j.procs.2016.05.374.

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33

Shum, Kam Hong. "Effective fault tolerance for agent-based cluster computing." Journal of Systems and Software 48, no. 3 (1999): 189–96. http://dx.doi.org/10.1016/s0164-1212(99)00057-6.

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34

Govinda, K., and E. Sathiyamoorthy. "Agent Based Security for Cloud Computing using Obfuscation." Procedia Engineering 38 (2012): 125–29. http://dx.doi.org/10.1016/j.proeng.2012.06.018.

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35

Li, ZhenJiang, Cheng Chen, and Kai Wang. "Cloud Computing for Agent-Based Urban Transportation Systems." IEEE Intelligent Systems 26, no. 1 (2011): 73–79. http://dx.doi.org/10.1109/mis.2011.10.

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36

Akram, Waseem, Muaz Niazi, Laszlo Barna Iantovics, and Athanasios V. Vasilakos. "Towards Agent-Based Model Specification of Smart Grid: A Cognitive Agent-Based Computing Approach." Interdisciplinary Description of Complex Systems 17, no. 3 (2019): 546–85. http://dx.doi.org/10.7906/indecs.17.3.14.

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37

Zhao, Jun, and Tingyu Sheng. "Construction of Shared OCCA Model Based on Mobile Agent Technology." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 6 (2020): 795–803. http://dx.doi.org/10.2174/2352096512666191016120432.

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Background: The Open Cloud Computing Alliance (OCCA) strives for more Cloud Computing Service Providers (CCSP) to join the alliance. OCCA only requires CCSP to provide virtual computing resources and does not care about the methods of the underlying implementation, which leads the open-source cloud computing to a larger scale and more efficient. Due to the differences in service modes and service categories, the cloud computing platforms formed by CCSP are heterogeneous. How to implement tasks across platforms and ensure the quality of migration are the key issue for sharing the OCCA platform.

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38

Heena, Kousar, and R. Prasad Babu B. "Multi-Agent based MapReduce Model for Efficient Utilization of System Resources." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 504–14. https://doi.org/10.11591/ijeecs.v11.i2.pp504-514.

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Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or batch pro

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39

Dai, Yu, Qiuhong Zhang, and Lei Yang. "Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning." Applied Sciences 11, no. 17 (2021): 7993. http://dx.doi.org/10.3390/app11177993.

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Mobile edge computing is a new computing model, which pushes cloud computing power from centralized cloud to network edge. However, with the sinking of computing power, user mobility brings new challenges: since it is usually unstable, services should be dynamically migrated between multiple edge servers to maintain service performance, that is, user-perceived latency. Considering that Mobile Edge Computing is a highly distributed computing environment and it is difficult to synchronize information between servers, in order to ensure the real-time performance of the migration strategy, a virtu

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40

CAO, LONGBING. "INTEGRATING AGENT, SERVICE AND ORGANIZATIONAL COMPUTING." International Journal of Software Engineering and Knowledge Engineering 18, no. 05 (2008): 573–96. http://dx.doi.org/10.1142/s0218194008003805.

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Engineering open complex systems is challenging because of system complexities such as openness, the involvement of organizational factors and service delivery. It cannot be handled well by the single use of existing computing techniques such as agent-based computing and service-oriented computing. Due to the intrinsic organizational characteristics and the request of service delivery, an integrative computing paradigm combining agent, service, organizational and social computing can open complex systems more effectively engineering. In this paper, we briefly introduce an integrative computing

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41

Xue, Caijun, Hong Nie, Qingying Qiu, and Peien Feng. "A Peer-to-Peer Distributed Collaborative Optimization System." Scientific Programming 12, no. 2 (2004): 121–31. http://dx.doi.org/10.1155/2004/365010.

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It is difficult to solve design optimization problems of complex systems by using a traditional computing method because complex simulation processes usually lead to large-scale computation. Therefore the distributed computing technology based on decomposition-coordination theory has received much attention by design engineers. This paper studies a peer-to-peer collaborative optimization method based on distributed computing technology in order to examine flexible optimization. A new distributed collaborative optimization framework is proposed, and a coordination method is developed and used t

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42

Hongbing, Li, Meng Bo, and Chen Shifu. "An Agent-Based Approach for Constructing Software Systems of Virtual Simulation." International Journal of Virtual Reality 4, no. 4 (2000): 97–109. http://dx.doi.org/10.20870/ijvr.2000.4.4.2659.

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The design and construction of virtual reality environments involve technologies such as computer graphics, image processing, pattern recognition, intelligent interface, artificial intelligence, voice recognition, network, parallel processing, and high-performance computing. Some researchers insist that object-oriented and agent-oriented technologies are fundamental for virtual reality system design. This paper applies artificial intelligence to the design of virtual reality systems. Agents are constructed by using object-oriented methods and a set of underlying computing models, such as neura

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43

Gorodnichev, Mikhail. "Simulation model of traffic flow based on agent-based modeling." Heritage and Sustainable Development 4, no. 2 (2022): 195–90. http://dx.doi.org/10.37868/hsd.v4i2.149.

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The work is devoted to traffic flow simulation using agent-based modeling using distributed computing. The role of modeling traffic flows in the modern world is considered. The types of traffic flow modeling are considered. The developed software makes it possible to simulate the movement and interaction of vehicles.

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44

Ganzha, Maria, Marcin Paprzycki, Costin Badica, and Rainer Unland. "Special issue: Agent Based Computing: From Model to Implementation." Multiagent and Grid Systems 7, no. 6 (2011): 183–85. http://dx.doi.org/10.3233/mgs-2011-0174.

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45

LIAO, Bei-Shui. "A Defeasible Logic-Based Flexible Agent for Autonomic Computing." Journal of Software 19, no. 3 (2008): 605–20. http://dx.doi.org/10.3724/sp.j.1001.2008.00605.

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46

Singh, Aarti, Dimple Juneja, and Manisha Malhotra. "Autonomous Agent Based Load Balancing Algorithm in Cloud Computing." Procedia Computer Science 45 (2015): 832–41. http://dx.doi.org/10.1016/j.procs.2015.03.168.

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47

Jin, Yingwei, Wenyu Qu, Yong Zhang, and Yong Wang. "A mobile agent-based routing model for grid computing." Journal of Supercomputing 63, no. 2 (2011): 431–42. http://dx.doi.org/10.1007/s11227-011-0616-2.

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48

Turek, Wojciech, Jan Stypka, Daniel Krzywicki, et al. "Highly scalable Erlang framework for agent-based metaheuristic computing." Journal of Computational Science 17 (November 2016): 234–48. http://dx.doi.org/10.1016/j.jocs.2016.03.003.

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49

Antonopoulos, Nick, Kyriakos Koukoumpetsos, and Alex Shafarenko. "Access control for agent‐based computing: a distributed approach." Internet Research 11, no. 1 (2001): 55–64. http://dx.doi.org/10.1108/10662240110365724.

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50

Saxena, Kritika, and Abhijit R. Abhyankar. "Agent-Based Distributed Computing for Power System State Estimation." IEEE Transactions on Smart Grid 11, no. 6 (2020): 5193–202. http://dx.doi.org/10.1109/tsg.2020.3006932.

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